Inference in regression models of heavily skewed alcohol use data: A comparison of ordinary least squares, generalized linear models, and bootstrap resampling
Inference in regression models of heavily skewed alcohol use data: A comparison of ordinary least squares, generalized linear models, and bootstrap resampling
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DOI：
10.1037/0893-164x.21.4.441
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发表时间：
2007-12-01
影响因子：
3.4
通讯作者：
Simons, Jeffrey S.
中科院分区：
文献类型：
--
作者：
Neal, Dan J.;Simons, Jeffrey S.
使用普通最小二乘回归模型分析酒精使用数据和其他低基本率风险行为可能存在问题。本文提出了两种替代的统计方法，广义线性模型和自举，这可能更适合这些数据。首先，介绍了这些方法背后的基本理论。然后，使用酒精使用行为和后果的数据集，基于这些方法的结果进行了对比，从普通最小二乘回归的结果。不太传统的方法始终表现出更好的拟合模型假设，如残差的图形分析所示，并确定了更重要的变量，可能导致理论上不同的解释模型的酒精使用。总之，这些模型显示出显着的承诺，进一步了解酒精相关的行为。
Analysis of alcohol use data and other low base rate risk behaviors using ordinary least squares regression models can be problematic. This article presents 2 alternative statistical approaches, generalized linear models and bootstrapping, that may be more appropriate for such data. First, the basic theory behind the approaches is presented. Then, using a data set of alcohol use behaviors and consequences, results based on these approaches are contrasted with the results from ordinary least squares regression. The less traditional approaches consistently demonstrated better fit with model assumptions, as demonstrated by graphical analysis of residuals, and identified more significant variables potentially resulting in theoretically different interpretations of the models of alcohol use. In conclusion, these models show significant promise for furthering the understanding of alcohol-related behaviors.